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STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product

by 1, 2,3,*, 1, 2,3 and 1
1
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
2
Department of Natural Resources and Environmental Sciences, College of Agriculture, Consumer, and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
3
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(19), 3209; https://doi.org/10.3390/rs12193209
Received: 22 August 2020 / Revised: 29 September 2020 / Accepted: 29 September 2020 / Published: 1 October 2020
(This article belongs to the Section Remote Sensing Image Processing)
Remote sensing datasets with both high spatial and high temporal resolution are critical for monitoring and modeling the dynamics of land surfaces. However, no current satellite sensor could simultaneously achieve both high spatial resolution and high revisiting frequency. Therefore, the integration of different sources of satellite data to produce a fusion product has become a popular solution to address this challenge. Many methods have been proposed to generate synthetic images with rich spatial details and high temporal frequency by combining two types of satellite datasets—usually frequent coarse-resolution images (e.g., MODIS) and sparse fine-resolution images (e.g., Landsat). In this paper, we introduce STAIR 2.0, a new fusion method that extends the previous STAIR fusion framework, to fuse three types of satellite datasets, including MODIS, Landsat, and Sentinel-2. In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. The multiple refined time series are then integrated stepwisely, from coarse- to fine- and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. We applied STAIR 2.0 to generate a 10-m, daily, cloud-/gap-free time series that covers the 2017 growing season of Saunders County, Nebraska. Moreover, the framework is generic and can be extended to integrate more types of satellite data sources, further improving the quality of the fusion product. View Full-Text
Keywords: fusion; MODIS; Landsat; Sentinel-2 fusion; MODIS; Landsat; Sentinel-2
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MDPI and ACS Style

Luo, Y.; Guan, K.; Peng, J.; Wang, S.; Huang, Y. STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product. Remote Sens. 2020, 12, 3209. https://doi.org/10.3390/rs12193209

AMA Style

Luo Y, Guan K, Peng J, Wang S, Huang Y. STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product. Remote Sensing. 2020; 12(19):3209. https://doi.org/10.3390/rs12193209

Chicago/Turabian Style

Luo, Yunan, Kaiyu Guan, Jian Peng, Sibo Wang, and Yizhi Huang. 2020. "STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product" Remote Sensing 12, no. 19: 3209. https://doi.org/10.3390/rs12193209

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